res.raw[1,"upper"]<-t.test(Male$open[Male$control==1])$conf.int[2]
res.raw[4,"estimate"]<-mean(Female$open[Female$control==1],na.rm=T)
res.raw[4,"lower"]<-t.test(Female$open[Female$control==1])$conf.int[1]
res.raw[4,"upper"]<-t.test(Female$open[Female$control==1])$conf.int[2]
# Objectives Prime
res.raw[2,"estimate"]<-mean(Male$open[Male$PRC==1],na.rm=T)
res.raw[2,"lower"]<-t.test(Male$open[Male$PRC==1])$conf.int[1]
res.raw[2,"upper"]<-t.test(Male$open[Male$PRC==1])$conf.int[2]
res.raw[5,"estimate"]<-mean(Female$open[Female$PRC==1],na.rm=T)
res.raw[5,"lower"]<-t.test(Female$open[Female$PRC==1])$conf.int[1]
res.raw[5,"upper"]<-t.test(Female$open[Female$PRC==1])$conf.int[2]
# Social Prime
res.raw[3,"estimate"]<-mean(Male$open[Male$interpersonal==1],na.rm=T)
res.raw[3,"lower"]<-t.test(Male$open[Male$interpersonal==1])$conf.int[1]
res.raw[3,"upper"]<-t.test(Male$open[Male$interpersonal==1])$conf.int[2]
res.raw[6,"estimate"]<-mean(Female$open[Female$interpersonal==1],na.rm=T)
res.raw[6,"lower"]<-t.test(Female$open[Female$interpersonal==1])$conf.int[1]
res.raw[6,"upper"]<-t.test(Female$open[Female$interpersonal==1])$conf.int[2]
res.raw<-as.data.frame(res.raw)
res.raw[,3]<-as.numeric(as.character(res.raw[,3]))
res.raw[,4]<-as.numeric(as.character(res.raw[,4]))
res.raw[,5]<-as.numeric(as.character(res.raw[,5]))
min(res.raw$lower)
max(res.raw$upper)
pdf("Log/Figure7b.pdf",width=8)
par(mar=c(5, 5, 3, 2) + 0.1)
x<-1:3
plot(x-0.1,res.raw[1:3,3],ylim=c(-0.05,0.6),col = 1,xlim=c(0.4,3.6),
main = "Open Discussion by Gender - Aged over 30",
ylab = "Probability of open discussion",cex.axis=1.5,
pch=16, cex.lab=1.5,xaxt="n",xlab="",cex=2)
axis(1,1:3,labels=c("Control\n","Objectives\nPrime","Social\nPrime"),cex.axis=1.5,mgp=c(3,3,0))
points(x+0.1,res.raw[4:6,3],pch=6)
for(x in 1:3){
segments(x-0.1,res.raw[x,4],x-0.1,res.raw[x,5],lwd=2, col=1, lty=1:1)
segments(x+0.1,res.raw[x+3,4],x+0.1,res.raw[x+3,5],lwd=2, col=1, lty=2:2)
}
legend("topright",legend = c("Men","Women"), col = 1, lty = 1:2, cex = 1.5, lwd = 2:2, bty="n", pch=c(16, 6))
dev.off()
## Figure 7c: effect of objectives prime by gender and age
rm(Female,Male,res.raw)
Male<-d[d$female==0,]
Female<-d[d$female==1,]
res_effects<-matrix(NA,nrow=4, ncol=4)
colnames(res_effects)<-c("estimate","s.e.","lower","upper")
row.names(res_effects)<-c("male_below30","male_over30","female_below30","female_over30")
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + ProfTeacher + ccp +colleague_everyday + misChinahigh  + CIYearsOverOne + current + JoinMotivations_material + TrainingOverMonth, data= Male[Male$AgeOver30==0,])
res_effects[1,1:2]<-coeftest(lgt.nc2)[2,1:2]
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + ProfTeacher + ccp  +colleague_everyday + misChinahigh  + CIYearsOverOne + current + JoinMotivations_material + TrainingOverMonth, data= Male[Male$AgeOver30==1,])
res_effects[2,1:2]<-coeftest(lgt.nc2)[2,1:2]
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + ProfTeacher + ccp  + colleague_everyday + misChinahigh  + CIYearsOverOne + current + JoinMotivations_material + TrainingOverMonth, data= Female[Female$AgeOver30==0,])
res_effects[3,1:2]<-coeftest(lgt.nc2)[2,1:2]
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + colleague_everyday + CIYearsOverOne + current + JoinMotivations_material, data= Female[Female$AgeOver30==1,])
res_effects[4,1:2]<-coeftest(lgt.nc2)[2,1:2]
res_effects<-as.data.frame(res_effects)
res_effects$lower<-res_effects[,1] - 1.96*res_effects[,2]
res_effects$upper<-res_effects[,1] + 1.96*res_effects[,2]
pdf("Log/Figure7c.pdf",width=8)
par(mar=c(5, 5, 3, 2) + 0.1)
x<-1:2
plot(x-0.1,res_effects[1:2,1],ylim=c(-0.6,0.4),xlim=c(0.4,2.6),main = "Effect of Objectives Prime by Gender and Age",
ylab = "Effect of objectives prime on open discussion",
pch=16,cex.lab=1.5,cex.axis=1.5, xaxt="n",xlab="",cex=2)
points(x+0.1,res_effects[3:4,1],pch=6)
axis(1,1:2,labels=c("Aged 30\nor below","Aged over\n30"),cex.axis=1.5,mgp=c(3,4,0))
abline(h=0,lty=2)
for(i in 1:2){
segments(i-0.1,res_effects[i,3],i-0.1,res_effects[i,4],lwd=2)
segments(i+0.1,res_effects[i+2,3],i+0.1,res_effects[i+2,4],lwd=2,lty=2)
}
legend("topright",legend = c("Men","Women"), col = 1, lty = 1:2, cex = 1.5, lwd = 2:2, bty="n", pch=c(16, 6))
dev.off()
## Figure 7d: effect of social prime by gender and age
rm(lgt.nc2)
res_effects<-matrix(NA,nrow=4, ncol=4)
colnames(res_effects)<-c("estimate","s.e.","lower","upper")
row.names(res_effects)<-c("male_below30","male_over30","female_below30","female_over30")
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + colleague_everyday + ProfTeacher + misChinahigh  + CIYearsOverOne + current + ccp +  JoinMotivations_material + TrainingOverMonth, data= Male[Male$AgeOver30==0,])
res_effects[1,1:2]<-coeftest(lgt.nc2)[3,1:2]
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + colleague_everyday + ProfTeacher + misChinahigh  + CIYearsOverOne + current + ccp+ JoinMotivations_material + TrainingOverMonth, data= Male[Male$AgeOver30==1,])
res_effects[2,1:2]<-coeftest(lgt.nc2)[3,1:2]
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + colleague_everyday + ProfTeacher + misChinahigh  + CIYearsOverOne + current + ccp + JoinMotivations_material + TrainingOverMonth, data= Female[Female$AgeOver30==0,])
res_effects[3,1:2]<-coeftest(lgt.nc2)[3,1:2]
lgt.nc2 <- lm(open ~ PRC + interpersonal + Grad + TeachYearsBefore0 + Chn_news_freq + Local_news_freq + colleague_everyday + current + JoinMotivations_material, data= Female[Female$AgeOver30==1,])
res_effects[4,1:2]<-coeftest(lgt.nc2)[3,1:2]
res_effects<-as.data.frame(res_effects)
res_effects$lower<-res_effects[,1] - 1.96*res_effects[,2] #lower bound of 95% CI
res_effects$upper<-res_effects[,1] + 1.96*res_effects[,2] #upper bound of 95% CI
pdf("Log/Figure7d.pdf",width=8)
par(mar=c(5, 5, 3, 2) + 0.1)
x<-1:2
plot(x-0.1,res_effects[1:2,1],ylim=c(-0.6,0.4),xlim=c(0.4,2.6),main = "Effect of Social Prime by Gender and Age",
ylab = "Effect of social prime on open discussion",
pch=16,cex.lab=1.5,cex.axis=1.5, xaxt="n",xlab="",cex=2)
points(x+0.1,res_effects[3:4,1],pch=6)
axis(1,1:2,labels=c("Aged 30\nor below","Aged over\n30"),cex.axis=1.5,mgp=c(3,4,0))
abline(h=0,lty=2)
for(i in 1:2){
segments(i-0.1,res_effects[i,3],i-0.1,res_effects[i,4],lwd=2)
segments(i+0.1,res_effects[i+2,3],i+0.1,res_effects[i+2,4],lwd=2,lty=2)
}
legend("topright",legend = c("Men","Women"), col = 1, lty = 1:2, cex = 1.5, lwd = 2:2, bty="n", pch=c(16, 6))
dev.off()
#########################
### Table A11 ###
#########################
rm(res_effects,Male,Female,lgt.nc2,i,x)
# Column 1: self-censor
m1<-logitmfx(SelfCensor ~ PRC*female*ccp + interpersonal*female*ccp + Grad +  TeachYearsBefore0 + AgeOver30 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef1<-m1$mfxest[,1]
se1<-m1$mfxest[,2]
# Column 2: one-sided
m2<-logitmfx(PosTaking1 ~ PRC*female*ccp + interpersonal*female*ccp + Grad + TrainingOverMonth + AgeOver30 +  TeachYearsBefore0 + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef2<-m2$mfxest[,1]
se2<-m2$mfxest[,2]
# Column 3: two-sided
m3<-logitmfx(PosTaking2 ~ PRC*female*ccp + interpersonal*female*ccp + Grad + TeachYearsBefore0 + AgeOver30 + current + JoinMotivations_material, data = d, atmean=F)
coef3<-m3$mfxest[,1]
se3<-m3$mfxest[,2]
# Column 4: open discussion
m4<-logitmfx(open ~ PRC*female*ccp + interpersonal*female*ccp + Grad +  TeachYearsBefore0 + AgeOver30 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef4<-m4$mfxest[,1]
se4<-m4$mfxest[,2]
# Baseline rates:
control_SelfCensor<-round(mean(d$SelfCensor[d$female==0 & d$control==1 & d$ccp==0],na.rm=T),digits=2)
control_Onesided<-round(mean(d$PosTaking1[d$female==0 & d$control==1 & d$ccp==0],na.rm=T),digits=2)
control_Twosided<-round(mean(d$PosTaking2[d$female==0 & d$control==1 & d$ccp==0],na.rm=T),digits=2)
control_Open<-round(mean(d$open[d$female==0 & d$control==1 & d$ccp==0],na.rm=T),digits=2)
# Write the table
TableA11<-stargazer(m1$fit, m2$fit, m3$fit, m4$fit,
coef=list(coef1,coef2,coef3,coef4),
se=list(se1,se2,se3,se4),
covariate.labels = c("Objectives * female * ccp", "Social * female * ccp", "Objectives * female", "Objectives * ccp", "female * ccp", "Social * female", "Social * ccp", "Objectives", "Social", "Female","CCP"),
order = c(17,18,12,13,14,15,16,1,4,2,3),
title=c("Table A11: Difference in gendered effects between CCP vs. non-CCP members"), digits=3,
type="text", align=TRUE, model.names=F, model.numbers = F,
dep.var.labels = c("Self-censor","One-sided","Two-sided","Open discussion"), dep.var.labels.include = T, dep.var.caption ="", omit.stat=c("all"), omit=c("Constant","Grad","TeachYearsBefore0","Chn_news_freq","Local_news_freq","InClassVig","ChnPol_news_freq","ProfTeacher", "colleague_everyday","CIYearsOverOne","current","JoinMotivations_material","TrainingOverMonth", "AgeOver30", "misChinahigh","US"),
no.space = T, header = F, add.lines = list(c("Control rate in male and non-CCP group",control_SelfCensor, control_Onesided, control_Twosided, control_Open), c("Observation",rep(429,4))))
write.table(TableA11, file="Log/TableA11.txt", row.names = FALSE,quote=F)
#########################
### Table A12 ###
#########################
rm(m1,m2,m3,m4,coef1,coef2,coef3,coef4,se1,se2,se3,se4,control_Onesided,control_Open,control_SelfCensor,control_Twosided,TableA11)
# Column 1: effect on open discussion among non-CCP members
dd<-d[d$ccp==0  & !is.na(d$ccp),]
m1<-logitmfx(dd$open ~ PRC*female + interpersonal*female + Grad +  TeachYearsBefore0 + AgeOver30 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = dd, atmean=F)
coef1<-m1$mfxest[,1]; se1<-m1$mfxest[,2]; obs1<-sum(!is.na(dd$open))
rm(dd)
# Column 2: effect on open discussion among CCP members
dd<-d[d$ccp==1 & !is.na(d$ccp),]
m2<-logitmfx(dd$open ~ PRC*female + interpersonal*female + Grad +  TeachYearsBefore0 + AgeOver30 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = dd, atmean=F)
coef2<-m2$mfxest[,1]; se2<-m2$mfxest[,2]; obs2<-sum(!is.na(dd$open))
# Baseline rates:
control_1<-round(mean(d$open[d$female==0 & d$control==1 & d$ccp==0],na.rm=T),digits=2)
control_2<-round(mean(d$open[d$female==0 & d$control==1 & d$ccp==1],na.rm=T),digits=2)
# Write the table
TableA12<-stargazer(m1$fit, m2$fit,
coef=list(coef1,coef2),
se=list(se1,se2),
covariate.labels = c("Objectives * female", "Social * female", "Objectives", "Social", "Female"),
order = c(11,12,1,3,2),
title=c("Table A12: Gender-based heterogeneous effects by CCP membership"), digits=3,
type="text", align=TRUE, model.names=F, model.numbers = F,
column.labels = c("non-CCP", "CCP"),
dep.var.labels.include = F, dep.var.caption ="Open discussion", omit.stat=c("all"), omit=c("Constant","Grad","TeachYearsBefore0","Chn_news_freq","Local_news_freq","InClassVig","ChnPol_news_freq","ProfTeacher", "colleague_everyday","CIYearsOverOne","current","JoinMotivations_material","TrainingOverMonth", "AgeOver30", "misChinahigh","US"),
no.space = T, header = F, add.lines = list(c("Control rate among men", control_1, control_2), c("Observation", obs1, obs2)))
write.table(TableA12, file="Log/TableA12.txt", row.names = FALSE,quote=F)
#########################
### Table A13 ###
#########################
rm(dd,m1,m2,coef1,coef2,se1,se2,TableA12,obs1,obs2,control_1,control_2)
Male<-d[d$female==0,]
# Column 1: Self-censor
ols.nc1 <- lm(SelfCensor ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + Grad + AgeOver30  + TeachYearsBefore0 + misChinahigh  + ccp + Chn_news_freq + Local_news_freq + ProfTeacher + US + colleague_everyday + CIYearsOverOne + current + JoinMotivations_material + TrainingOverMonth, data = Male)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m1<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Column 2: One-sided
ols.nc1 <- lm(PosTaking1 ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + Grad + ProfTeacher+ misChinahigh + TrainingOverMonth + Chn_news_freq + US+ colleague_everyday + CIYearsOverOne + current, data = Male)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m2<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Column 3: Two-sided
ols.nc1 <- lm(PosTaking2 ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + Grad + AgeOver30 +  TeachYearsBefore0 + misChinahigh  + ccp + Chn_news_freq + Local_news_freq + ProfTeacher + US + colleague_everyday + CIYearsOverOne + current + JoinMotivations_material + TrainingOverMonth, data = Male)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m3<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Column 4: Open discussion
ols.nc1<- lm(open ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + Grad + AgeOver30 + misChinahigh  + ccp + Chn_news_freq + Local_news_freq + current+ CIYearsOverOne + JoinMotivations_material +TrainingOverMonth, data = Male)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m4<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Baseline rates:
control_SelfCensor<-round(mean(Male$SelfCensor[Male$control==1 & Male$Prv_stu==1],na.rm=T),digits=2)
control_Onesided<-round(mean(Male$PosTaking1[Male$control==1 & Male$Prv_stu==1],na.rm=T),digits=2)
control_Twosided<-round(mean(Male$PosTaking2[Male$control==1 & Male$Prv_stu==1],na.rm=T),digits=2)
control_Open<-round(mean(Male$open[Male$control==1 & Male$Prv_stu==1],na.rm=T),digits=2)
# Write the table
TableA13<-stargazer(m1,m2,m3,m4,
coef=list(m1[,1],m2[,1],m3[,1],m4[,1]),
se=list(m1[,2],m2[,2],m3[,2],m4[,2]),
covariate.labels = c("Objectives * in-class", "Objectives * private colleague", "Social * in-class", "Social * private colleague", "Objectives Prime", "Social Prime", "In-class vignette", "Private colleague vignette"),
order = c(19,21,20,22,1,3,2,4),
title=c("Table A13: Heterogeneous effects by vignettes among men"), digits=3,
type="text", align=TRUE, model.names=F, model.numbers = F, dep.var.caption = "",
column.labels = c("Self-censor","One-sided","Two-sided","Open discussion"),
dep.var.labels.include = T, omit.stat=c("all"), omit=c("Constant","Grad","TeachYearsBefore0","ccp","Chn_news_freq","Local_news_freq","ChnPol_news_freq","ProfTeacher", "colleague_everyday","CIYearsOverOne","current","JoinMotivations_material","TrainingOverMonth", "AgeOver30", "misChinahigh","US"),
no.space = T, header = F, add.lines = list(c("Control rate in private student vignette", control_SelfCensor, control_Onesided, control_Twosided, control_Open), c("Observation", rep(sum(!is.na(Male$SelfCensor)),4))))
write.table(TableA13, file="Log/TableA13.txt", row.names = FALSE,quote=F)
#########################
### Table A14 ###
#########################
rm(m1,m2,m3,m4,Male,ols.nc1,vcovWhite.nc1,control_Onesided,control_Open,control_SelfCensor,control_Twosided,TableA13)
Female<-d[d$female==1,]
# Column 1: Self-censor
ols.nc1 <- lm(SelfCensor ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + JoinMotivations_material + CIYearsOverOne + AgeOver30, data = Female)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m1<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Column 2: One-sided
ols.nc1 <- lm(PosTaking1 ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + JoinMotivations_material + CIYearsOverOne + AgeOver30, data = Female)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m2<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Column 3: Two-sided
ols.nc1 <- lm(PosTaking2 ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + JoinMotivations_material + CIYearsOverOne + AgeOver30, data = Female)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m3<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Column 4: Open discussion
ols.nc1 <- lm(open ~ PRC*InClassVig  + interpersonal*InClassVig + PRC*Prv_clg + interpersonal*Prv_clg + misChinahigh  + colleague_everyday + JoinMotivations_material + TrainingOverMonth, data = Female)
vcovWhite.nc1<-vcovHC(ols.nc1, type="HC1")
m4<-coeftest(ols.nc1, vcov = vcovWhite.nc1)
# Baseline rates
control_SelfCensor<-round(mean(Female$SelfCensor[Female$control==1 & Female$Prv_stu==1],na.rm=T),digits=3)
control_Onesided<-round(mean(Female$PosTaking1[Female$control==1 & Female$Prv_stu==1],na.rm=T),digits=3)
control_Twosided<-round(mean(Female$PosTaking2[Female$control==1 & Female$Prv_stu==1],na.rm=T),digits=3)
control_Open<-round(mean(Female$open[Female$control==1 & Female$Prv_stu==1],na.rm=T),digits=3)
# Write the table
TableA14<-stargazer(m1,m2,m3,m4,
coef=list(m1[,1],m2[,1],m3[,1],m4[,1]),
se=list(m1[,2],m2[,2],m3[,2],m4[,2]),
covariate.labels = c("Objectives * in-class", "Objectives * private colleague", "Social * in-class", "Social * private colleague", "Objectives Prime", "Social Prime", "In-class vignette", "Private colleague vignette"),
order = c(11,13,12,14,1,3,2,4),
title=c("Table A14: Heterogeneous effects by vignettes among women"), digits=3,
type="text", align=TRUE, model.names=F, model.numbers = F, dep.var.caption = "",
column.labels = c("Self-censor","One-sided","Two-sided","Open discussion"),
dep.var.labels.include = T, omit.stat=c("all"),
omit=c("Constant","Grad","TeachYearsBefore0","ccp","Chn_news_freq","Local_news_freq","ChnPol_news_freq","ProfTeacher", "colleague_everyday","CIYearsOverOne","current","JoinMotivations_material","TrainingOverMonth", "AgeOver30", "misChinahigh","US"),
no.space = T, header = F, add.lines = list(c("Control rate in private student vignette", control_SelfCensor, control_Onesided, control_Twosided, control_Open), c("Observation", rep(sum(!is.na(Female$SelfCensor)),4))))
write.table(TableA14, file="Log/TableA14.txt", row.names = FALSE,quote=F)
#########################
### Table A15 ###
#########################
rm(m1,m2,m3,m4,Female,ols.nc1,vcovWhite.nc1,control_Onesided,control_Open,control_SelfCensor,control_Twosided,TableA14)
dd<-d[1:284,] #unique respondents
dd<-dd[dd$female==1,] #women respondents
dcontrol<-dd[dd$control==1,] #Control
dPRC<-dd[dd$PRC==1,] #Objectives treatment group
dInterpersonal<-dd[dd$interpersonal==1,] #Social treatment group
# Calculate means and p-values
Control<-c(mean(dcontrol$ProfTeacher,na.rm=T),
mean(dcontrol$age,na.rm=T),
mean(dcontrol$ccp,na.rm=T),
mean(dcontrol$Grad,na.rm=T),
mean(dcontrol$CIYears_num,na.rm=T),
mean(dcontrol$TeacherBefore,na.rm=T),
mean(dcontrol$TrainingOverMonth,na.rm=T),
mean(dcontrol$LocalMediaFriend,na.rm=T))
Objectives<-c(mean(dPRC$ProfTeacher,na.rm=T),
mean(dPRC$age,na.rm=T),
mean(dPRC$ccp,na.rm=T),
mean(dPRC$Grad,na.rm=T),
mean(dPRC$CIYears_num,na.rm=T),
mean(dPRC$TeacherBefore,na.rm=T),
mean(dPRC$TrainingOverMonth,na.rm=T),
mean(dPRC$LocalMediaFriend,na.rm=T))
Social<-c(mean(dInterpersonal$ProfTeacher,na.rm=T),
mean(dInterpersonal$age,na.rm=T),
mean(dInterpersonal$ccp,na.rm=T),
mean(dInterpersonal$Grad,na.rm=T),
mean(dInterpersonal$CIYears_num,na.rm=T),
mean(dInterpersonal$TeacherBefore,na.rm=T),
mean(dInterpersonal$TrainingOverMonth,na.rm=T),
mean(dInterpersonal$LocalMediaFriend,na.rm=T))
p_ProfTeacher<-pf(summary(lm(ProfTeacher~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(ProfTeacher~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(ProfTeacher~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_age<-pf(summary(lm(age~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(age~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(age~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_ccp<-pf(summary(lm(ccp~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(ccp~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(ccp~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_Grad<-pf(summary(lm(Grad~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(Grad~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(Grad~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_CIYears_num<-pf(summary(lm(CIYears_num~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(CIYears_num~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(CIYears_num~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_TeacherBefore<-pf(summary(lm(TeacherBefore~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(TeacherBefore~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(TeacherBefore~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_TrainingOverMonth<-pf(summary(lm(TrainingOverMonth~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(TrainingOverMonth~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(TrainingOverMonth~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_LocalMediaFriend<-pf(summary(lm(LocalMediaFriend~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(LocalMediaFriend~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(LocalMediaFriend~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
Pvalue<-c(p_ProfTeacher,p_age,p_ccp,p_Grad,p_CIYears_num,p_TeacherBefore,p_TrainingOverMonth, p_LocalMediaFriend)
obs<-c(nrow(dcontrol),nrow(dPRC),nrow(dInterpersonal),"-")
# Write the table
balance<-cbind.data.frame(Control,Objectives,Social,Pvalue)
balance<-round(balance,digits=3)
balance[2,1:3]<-round(balance[2,1:3],digits=1) #rounding age to 0.1 decimal points
balance[9,]<-obs
colnames(balance)<-c("Control","Objectives","Social","P-value")
rownames(balance)<-c("Seniority in CI", "Age", "CCP member", "Graduate degree", "Years at CI", "Teaching before CI (Y/N)", "Hanban training over 1 month", "Perceived friendliness of host-country media", "Number of respondents")
TableA15<-stargazer(balance,title="Table A15: Balance of Covariates within Women CI teachers",
type="text",summary=FALSE, digits=3)
write.table(TableA15, file="Log/TableA15.txt", row.names = FALSE, quote=F)
#########################
### Table A16 ###
#########################
rm(dd,dcontrol,dPRC,dInterpersonal,balance,Control,Objectives,Social,Pvalue,obs,p_age,p_ccp,p_Grad,TableA15)
rm(p_CIYears_num,p_LocalMediaFriend,p_ProfTeacher,p_TeacherBefore,p_TrainingOverMonth)
dd<-d[1:284,] #unique respondents
dd<-dd[dd$female==0,] #men respondents
dcontrol<-dd[dd$control==1,] #Control
dPRC<-dd[dd$PRC==1,] #Objectives treatment group
dInterpersonal<-dd[dd$interpersonal==1,] #Social treatment group
# Calculate means and p-values
Control<-c(mean(dcontrol$ProfTeacher,na.rm=T),
mean(dcontrol$age,na.rm=T),
mean(dcontrol$ccp,na.rm=T),
mean(dcontrol$Grad,na.rm=T),
mean(dcontrol$CIYears_num,na.rm=T),
mean(dcontrol$TeacherBefore,na.rm=T),
mean(dcontrol$TrainingOverMonth,na.rm=T),
mean(dcontrol$LocalMediaFriend,na.rm=T))
Objectives<-c(mean(dPRC$ProfTeacher,na.rm=T),
mean(dPRC$age,na.rm=T),
mean(dPRC$ccp,na.rm=T),
mean(dPRC$Grad,na.rm=T),
mean(dPRC$CIYears_num,na.rm=T),
mean(dPRC$TeacherBefore,na.rm=T),
mean(dPRC$TrainingOverMonth,na.rm=T),
mean(dPRC$LocalMediaFriend,na.rm=T))
Social<-c(mean(dInterpersonal$ProfTeacher,na.rm=T),
mean(dInterpersonal$age,na.rm=T),
mean(dInterpersonal$ccp,na.rm=T),
mean(dInterpersonal$Grad,na.rm=T),
mean(dInterpersonal$CIYears_num,na.rm=T),
mean(dInterpersonal$TeacherBefore,na.rm=T),
mean(dInterpersonal$TrainingOverMonth,na.rm=T),
mean(dInterpersonal$LocalMediaFriend,na.rm=T))
p_ProfTeacher<-pf(summary(lm(ProfTeacher~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(ProfTeacher~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(ProfTeacher~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_age<-pf(summary(lm(age~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(age~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(age~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_ccp<-pf(summary(lm(ccp~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(ccp~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(ccp~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_Grad<-pf(summary(lm(Grad~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(Grad~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(Grad~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_CIYears_num<-pf(summary(lm(CIYears_num~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(CIYears_num~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(CIYears_num~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_TeacherBefore<-pf(summary(lm(TeacherBefore~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(TeacherBefore~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(TeacherBefore~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_TrainingOverMonth<-pf(summary(lm(TrainingOverMonth~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(TrainingOverMonth~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(TrainingOverMonth~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
p_LocalMediaFriend<-pf(summary(lm(LocalMediaFriend~PRC+interpersonal,data=dd))$fstatistic[1],summary(lm(LocalMediaFriend~PRC+interpersonal,data=dd))$fstatistic[2],
summary(lm(LocalMediaFriend~PRC+interpersonal,data=dd))$fstatistic[3], lower.tail=FALSE)
Pvalue<-c(p_ProfTeacher,p_age,p_ccp,p_Grad,p_CIYears_num,p_TeacherBefore,p_TrainingOverMonth, p_LocalMediaFriend)
obs<-c(nrow(dcontrol),nrow(dPRC),nrow(dInterpersonal),"-")
# Write the table
balance<-cbind.data.frame(Control,Objectives,Social,Pvalue)
balance<-round(balance,digits=3)
balance[2,1:3]<-round(balance[2,1:3],digits=1) #rounding age to 0.1 decimal points
balance[9,]<-obs
colnames(balance)<-c("Control","Objectives","Social","P-value")
rownames(balance)<-c("Seniority in CI", "Age", "CCP member", "Graduate degree", "Years at CI", "Teaching before CI (Y/N)", "Hanban training over 1 month", "Perceived friendliness of host-country media", "Number of respondents")
TableA16<-stargazer(balance,title="Table A16: Balance of Covariates within Men CI teachers",
type="text",summary=FALSE, digits=3)
write.table(TableA16, file="Log/TableA16.txt", row.names = FALSE, quote=F)
#########################
### Table A17 ###
#########################
rm(dd,dcontrol,dPRC,dInterpersonal,balance,Control,Objectives,Social,Pvalue,obs,p_age,p_ccp,p_Grad,TableA16)
rm(p_CIYears_num,p_LocalMediaFriend,p_ProfTeacher,p_TeacherBefore,p_TrainingOverMonth)
dd<-d[1:284,] #unique respondents
Male<-dd[dd$female==0,]
Female<-dd[dd$female==1,]
# Calculate means of each gender and p-values
Male_cov <-c(mean(Male$ProfTeacher,na.rm=T),
mean(Male$age,na.rm=T),
mean(Male$ccp,na.rm=T),
mean(Male$Grad,na.rm=T),
mean(Male$CIYears_num,na.rm=T),
mean(Male$TeacherBefore,na.rm=T),
mean(Male$TrainingOverMonth,na.rm=T),
mean(Male$LocalMediaFriend,na.rm=T))
Female_cov <-c(mean(Female$ProfTeacher,na.rm=T),
mean(Female$age,na.rm=T),
mean(Female$ccp,na.rm=T),
mean(Female$Grad,na.rm=T),
mean(Female$CIYears_num,na.rm=T),
mean(Female$TeacherBefore,na.rm=T),
mean(Female$TrainingOverMonth,na.rm=T),
mean(Female$LocalMediaFriend,na.rm=T))
pvalue_cov<-c(t.test(Male$ProfTeacher, Female$ProfTeacher)$p.value,
t.test(Male$age, Female$age)$p.value,
t.test(Male$ccp, Female$ccp)$p.value,
t.test(Male$Grad, Female$Grad)$p.value,
t.test(Male$CIYears_num, Female$CIYears_num)$p.value,
t.test(Male$TeacherBefore, Female$TeacherBefore)$p.value,
t.test(Male$TrainingOverMonth, Female$TrainingOverMonth)$p.value,
t.test(Male$LocalMediaFriend, Female$LocalMediaFriend)$p.value)
obs<-c(nrow(Male),nrow(Female),"-")
# Write the table
gender_balance<-cbind.data.frame(Male_cov, Female_cov, pvalue_cov)
gender_balance<-round(gender_balance,digits=3)
gender_balance[2,1:2]<-round(gender_balance[2,1:2],digits=1) #rounding age to 0.1 decimal points
gender_balance[9,]<-obs
colnames(gender_balance)<-c("Men","Women","P-value")
rownames(gender_balance)<-c("Seniority in CI", "Age", "CCP member", "Graduate degree", "Years at CI", "Teaching before CI (Y/N)", "Hanban training over 1 month", "Perceived friendliness of host-country media", "Number of respondents")
TableA17<-stargazer(gender_balance,title="Table A17: Comparing Pre-treatment Covariates between Genders",
type="text",summary=FALSE, digits=3)
write.table(TableA17, file="Log/TableA17.txt", row.names = FALSE, quote=F)
#########################
### Table A18 ###
#########################
rm(dd,Female,Male,gender_balance,obs,Male_cov,Female_cov,pvalue_cov,TableA17)
## Step 1: Calculate difference in effect, SE, and unadjusted p-value for each of the 14 pre-registered covariates
coef<-matrix(NA,nrow=14,ncol=2); SE<-matrix(NA,nrow=14,ncol=2); pvalue<-matrix(NA,nrow=14,ncol=2)
row.names(coef)<-c("female","AgeOver30","ccp","Grad","TeachYearsBefore0","Chn_news_freq","Local_news_freq","ProfTeacher", "LocalMediaFriendOver8","colleague_everyday", "misChinahigh","agree_personality","mission_misunderstanding","US")
colnames(coef)<-c("Objectives","Social")
row.names(SE)<-c("female","AgeOver30","ccp","Grad","TeachYearsBefore0","Chn_news_freq","Local_news_freq","ProfTeacher", "LocalMediaFriendOver8","colleague_everyday", "misChinahigh","agree_personality","mission_misunderstanding","US")
colnames(SE)<-c("Objectives","Social")
row.names(pvalue)<-c("female","AgeOver30","ccp","Grad","TeachYearsBefore0","Chn_news_freq","Local_news_freq","ProfTeacher", "LocalMediaFriendOver8","colleague_everyday", "misChinahigh","agree_personality","mission_misunderstanding","US")
colnames(pvalue)<-c("Objectives","Social")
# Estimate the diff in effect btw genders (then repeat the same process for each covariate)
m1<-logitmfx(open ~ PRC*female + interpersonal*female + AgeOver30 + ccp + Grad + TeachYearsBefore0 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[1,]<-tail(m1$mfxest[,1],n=2);SE[1,]<-tail(m1$mfxest[,2],n=2)
pvalue[1,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*AgeOver30 + interpersonal*AgeOver30 + female + ccp + Grad + TeachYearsBefore0 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[2,]<-tail(m1$mfxest[,1],n=2);SE[2,]<-tail(m1$mfxest[,2],n=2)
pvalue[2,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*ccp + interpersonal*ccp + female + Grad + AgeOver30 + TeachYearsBefore0 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[3,]<-tail(m1$mfxest[,1],n=2);SE[3,]<-tail(m1$mfxest[,2],n=2)
pvalue[3,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*Grad + interpersonal*Grad + female + ccp + AgeOver30 + TeachYearsBefore0 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[4,]<-tail(m1$mfxest[,1],n=2);SE[4,]<-tail(m1$mfxest[,2],n=2)
pvalue[4,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*TeachYearsBefore0 + interpersonal*TeachYearsBefore0 + female + ccp + Grad + AgeOver30 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[5,]<-tail(m1$mfxest[,1],n=2);SE[5,]<-tail(m1$mfxest[,2],n=2)
pvalue[5,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*Chn_news_freq + interpersonal*Chn_news_freq + female + TeachYearsBefore0 + ccp + Grad + AgeOver30 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[6,]<-tail(m1$mfxest[,1],n=2);SE[6,]<-tail(m1$mfxest[,2],n=2)
pvalue[6,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*Local_news_freq + interpersonal*Local_news_freq + female + AgeOver30 + Grad + CIYearsOverOne + TrainingOverMonth + TeachYearsBefore0 + US + misChinahigh + Chn_news_freq, data = d, atmean=F)
coef[7,]<-tail(m1$mfxest[,1],n=2);SE[7,]<-tail(m1$mfxest[,2],n=2)
pvalue[7,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*ProfTeacher + interpersonal*ProfTeacher + female + AgeOver30 + ccp + TrainingOverMonth + CIYearsOverOne + TeachYearsBefore0 + US + misChinahigh+ Local_news_freq + Chn_news_freq, data = d, atmean=F)
coef[8,]<-tail(m1$mfxest[,1],n=2);SE[8,]<-tail(m1$mfxest[,2],n=2)
pvalue[8,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*LocalMediaFriendOver8 + interpersonal*LocalMediaFriendOver8 + female + TeachYearsBefore0 + ccp + Grad + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[9,]<-tail(m1$mfxest[,1],n=2);SE[9,]<-tail(m1$mfxest[,2],n=2)
pvalue[9,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*colleague_everyday + interpersonal*colleague_everyday + female + AgeOver30 + Grad +TeachYearsBefore0 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[10,]<-tail(m1$mfxest[,1],n=2);SE[10,]<-tail(m1$mfxest[,2],n=2)
pvalue[10,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*misChinahigh + interpersonal*misChinahigh + female + Grad + AgeOver30+TeachYearsBefore0 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[11,]<-tail(m1$mfxest[,1],n=2);SE[11,]<-tail(m1$mfxest[,2],n=2)
pvalue[11,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*agree_personality + interpersonal*agree_personality + female + ccp + Grad + AgeOver30 + TeachYearsBefore0 + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[12,]<-tail(m1$mfxest[,1],n=2);SE[12,]<-tail(m1$mfxest[,2],n=2)
pvalue[12,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*mission_misunderstanding + interpersonal*mission_misunderstanding + female + Grad + AgeOver30 + TeachYearsBefore0  + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[13,]<-tail(m1$mfxest[,1],n=2);SE[13,]<-tail(m1$mfxest[,2],n=2)
pvalue[13,]<-tail(m1$mfxest[,4],n=2);rm(m1)
m1<-logitmfx(open ~ PRC*US + interpersonal*US + female + Grad + AgeOver30 +  TeachYearsBefore0 + ccp + TrainingOverMonth + CIYearsOverOne + current + JoinMotivations_material, data = d, atmean=F)
coef[14,]<-tail(m1$mfxest[,1],n=2);SE[14,]<-tail(m1$mfxest[,2],n=2)
pvalue[14,]<-tail(m1$mfxest[,4],n=2);rm(m1)
## Step 2: Multiple testing correction on p-values
alpha<-0.05
# Benjamini-Hochberg correction (FDR)
BH_result<-p.adjust(pvalue[,1], "BH") < alpha
# Holm correction (FWER)
Holm_result<-p.adjust(pvalue[,1], "holm") < alpha
# Bonferroni correction
BF_result<-p.adjust(pvalue[,1], "bonferroni") < alpha
# Step 3: Write the table
TableA18<-cbind.data.frame(coef[,1], SE[,1], pvalue[,1], BH_result, Holm_result, BF_result)
TableA18[,1:3]<-round(TableA18[,1:3],digits=3)
colnames(TableA18)<-c("Estimate","SE","Unadjusted p-value","BH","Holm","BF")
rownames(TableA18)<-c("Women vs. Men","Age (>30 vs. <=30)","CCP vs. Non-CCP","Graduate vs. below Graduate",
"Work experience before CI (Yes vs. No)","Freq PRC news consumer (Yes vs. No)","Freq local news consumer (Yes vs. No)",
"Seniority in CI (Senior vs. Junior)","Perceived host-country media friendly (Yes vs. No)",
"Interact with host-country teacher everyday (Yes vs. No)","Perceived misunderstanding on China (high vs. low)",
"Self-reported agreeableness (Yes vs. No)","Perceived political mission (Yes vs. No)","US vs. Non-US")
TableA18_final<-stargazer(TableA18,title="Table A18: Difference in Effects of Objectives Prime",
type="text",summary=FALSE, digits=3)
write.table(TableA18_final, file="Log/TableA18.txt", row.names = FALSE, quote=F)
#########################
### Table A19 ###
#########################
rm(BH_result,Holm_result,BF_result,TableA18_final,TableA18)
## Multiple testing correction on p-values
alpha<-0.05
# Benjamini-Hochberg correction (FDR)
BH_result<-p.adjust(pvalue[,2], "BH") < alpha
# Holm correction (FWER)
Holm_result<-p.adjust(pvalue[,2], "holm") < alpha
# Bonferroni correction
BF_result<-p.adjust(pvalue[,2], "bonferroni") < alpha
# Step 3: Write the table
TableA19<-cbind.data.frame(coef[,2], SE[,2], pvalue[,2], BH_result, Holm_result, BF_result)
TableA19[,1:3]<-round(TableA19[,1:3],digits=3)
colnames(TableA19)<-c("Estimate","SE","Unadjusted p-value","BH","Holm","BF")
rownames(TableA19)<-c("Women vs. Men","Age (>30 vs. <=30)","CCP vs. Non-CCP","Graduate vs. below Graduate",
"Work experience before CI (Yes vs. No)","Freq PRC news consumer (Yes vs. No)","Freq local news consumer (Yes vs. No)",
"Seniority in CI (Senior vs. Junior)","Perceived host-country media friendly (Yes vs. No)",
"Interact with host-country teacher everyday (Yes vs. No)","Perceived misunderstanding on China (high vs. low)",
"Self-reported agreeableness (Yes vs. No)","Perceived political mission (Yes vs. No)","US vs. Non-US")
TableA19_final<-stargazer(TableA19,title="Table A19: Difference in Effects of Social Prime",
type="text",summary=FALSE, digits=3)
write.table(TableA19_final, file="Log/TableA19.txt", row.names = FALSE, quote=F)
